{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5317e4f-ffe0-4810-b32a-ba170c639d02",
   "metadata": {},
   "source": [
    "# PANDAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f209d1ae-2223-48b5-994a-a33f74d98bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "32b9d553-3096-49e7-976f-4e35ba046952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  70,  65, 100, 230, 150, 100, 300, 250, 150])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "times = np.array([6, 7, 8, 11, 12, 13, 18, 19, 20, 21])\n",
    "# 顾客人数\n",
    "customers = np.array([60, 70, 65, 100, 230, 150, 100, 300, 250, 150])\n",
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "473e405f-c60d-4fb2-b179-c662405f3f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers=pd.Series(customers,times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "47439f93-5310-4f81-9ba6-707786477055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6      60\n",
       "7      70\n",
       "8      65\n",
       "11    100\n",
       "12    230\n",
       "13    150\n",
       "18    100\n",
       "19    300\n",
       "20    250\n",
       "21    150\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5a8ea0b8-ef30-484f-8c0d-517b0e1d4148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(100)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers[11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c45e3a32-6244-4750-ab1e-0e3e19ca0618",
   "metadata": {},
   "outputs": [],
   "source": [
    "times = np.array(['6h', '7h', '8h', '11h', '12h', '13h', '18h', '19h', '20h', '21h'])\n",
    "# 顾客人数\n",
    "customers = np.array([60, 70, 65, 100, 230, 150, 100, 300, 250, 150])\n",
    "customers=pd.Series(data=customers,index=times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ab950613-5522-4d1e-9294-a8ddbccdf792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(150)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers['13h']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "43e33dfe-e260-4b11-859d-c0504fb907d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  70,  65, 100, 230, 150, 100, 300, 250, 150])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "94e92d2c-28f1-444d-8fe4-269582ec5937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['6h', '7h', '8h', '11h', '12h', '13h', '18h', '19h', '20h', '21h'], dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3b0653e4-35f1-42fc-9157-846795b2ec6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(60)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "544feb58-231c-465f-859d-4cfaa010141c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 时段4点，5点，... 22点\n",
    "times = np.array([6, 7, 8, 11, 12, 13, 18, 19, 20, 21])\n",
    "# 顾客人数\n",
    "customers = np.array([60, 70, 65, 100, 230, 150, 100, 300, 250, 150])\n",
    "# 平均消费\n",
    "costs = np.array([6, 7, 8, 24, 23, 26, 45, 55, 45, 40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "87e6ac65-40fd-4864-afc1-754bbf848658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customers</th>\n",
       "      <th>costs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>65</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>100</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>230</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>150</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>100</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>300</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>250</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>150</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    customers  costs\n",
       "6          60      6\n",
       "7          70      7\n",
       "8          65      8\n",
       "11        100     24\n",
       "12        230     23\n",
       "13        150     26\n",
       "18        100     45\n",
       "19        300     55\n",
       "20        250     45\n",
       "21        150     40"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data={'customers':customers,'costs':costs}\n",
    "df=pd.DataFrame(data,index=times)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "37f808fd-b6af-40f5-8f47-e194e4305cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(150)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['customers'][21]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79784d0-007a-4382-a8bb-f9ac31c8b026",
   "metadata": {},
   "source": [
    "## 尝试创建随机的Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "97373c38-8a9d-4db1-919d-482882bcb075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>politics</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>major</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>50</td>\n",
       "      <td>63</td>\n",
       "      <td>87</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63</td>\n",
       "      <td>67</td>\n",
       "      <td>53</td>\n",
       "      <td>64</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59</td>\n",
       "      <td>105</td>\n",
       "      <td>44</td>\n",
       "      <td>137</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>97</td>\n",
       "      <td>48</td>\n",
       "      <td>118</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64</td>\n",
       "      <td>115</td>\n",
       "      <td>56</td>\n",
       "      <td>91</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48</td>\n",
       "      <td>53</td>\n",
       "      <td>49</td>\n",
       "      <td>72</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50</td>\n",
       "      <td>66</td>\n",
       "      <td>42</td>\n",
       "      <td>136</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>59</td>\n",
       "      <td>66</td>\n",
       "      <td>47</td>\n",
       "      <td>119</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>68</td>\n",
       "      <td>57</td>\n",
       "      <td>63</td>\n",
       "      <td>69</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>54</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>84</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>53</td>\n",
       "      <td>73</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>65</td>\n",
       "      <td>73</td>\n",
       "      <td>46</td>\n",
       "      <td>98</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58</td>\n",
       "      <td>76</td>\n",
       "      <td>60</td>\n",
       "      <td>127</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "      <td>72</td>\n",
       "      <td>58</td>\n",
       "      <td>126</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>68</td>\n",
       "      <td>77</td>\n",
       "      <td>62</td>\n",
       "      <td>132</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>58</td>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>109</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>60</td>\n",
       "      <td>81</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>63</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>61</td>\n",
       "      <td>59</td>\n",
       "      <td>50</td>\n",
       "      <td>118</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>63</td>\n",
       "      <td>108</td>\n",
       "      <td>65</td>\n",
       "      <td>97</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>53</td>\n",
       "      <td>114</td>\n",
       "      <td>48</td>\n",
       "      <td>60</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>46</td>\n",
       "      <td>64</td>\n",
       "      <td>46</td>\n",
       "      <td>119</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>66</td>\n",
       "      <td>89</td>\n",
       "      <td>48</td>\n",
       "      <td>129</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>48</td>\n",
       "      <td>79</td>\n",
       "      <td>50</td>\n",
       "      <td>126</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>60</td>\n",
       "      <td>115</td>\n",
       "      <td>45</td>\n",
       "      <td>101</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>52</td>\n",
       "      <td>89</td>\n",
       "      <td>49</td>\n",
       "      <td>115</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>61</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>66</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>49</td>\n",
       "      <td>92</td>\n",
       "      <td>45</td>\n",
       "      <td>102</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>45</td>\n",
       "      <td>101</td>\n",
       "      <td>43</td>\n",
       "      <td>136</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>67</td>\n",
       "      <td>88</td>\n",
       "      <td>57</td>\n",
       "      <td>116</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>54</td>\n",
       "      <td>56</td>\n",
       "      <td>58</td>\n",
       "      <td>104</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>57</td>\n",
       "      <td>97</td>\n",
       "      <td>63</td>\n",
       "      <td>103</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>129</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>61</td>\n",
       "      <td>71</td>\n",
       "      <td>58</td>\n",
       "      <td>88</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>69</td>\n",
       "      <td>113</td>\n",
       "      <td>60</td>\n",
       "      <td>119</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>67</td>\n",
       "      <td>79</td>\n",
       "      <td>45</td>\n",
       "      <td>108</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>66</td>\n",
       "      <td>117</td>\n",
       "      <td>61</td>\n",
       "      <td>84</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>46</td>\n",
       "      <td>101</td>\n",
       "      <td>64</td>\n",
       "      <td>135</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>48</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "      <td>125</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>49</td>\n",
       "      <td>91</td>\n",
       "      <td>40</td>\n",
       "      <td>101</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    politics  math  english  major  total\n",
       "1         57    50       63     87    257\n",
       "2         63    67       53     64    247\n",
       "3         59   105       44    137    345\n",
       "4         50    97       48    118    313\n",
       "5         64   115       56     91    326\n",
       "6         48    53       49     72    222\n",
       "7         50    66       42    136    294\n",
       "8         59    66       47    119    291\n",
       "9         68    57       63     69    257\n",
       "10        54    64       58     84    260\n",
       "11        53    73       60     65    251\n",
       "12        65    73       46     98    282\n",
       "13        58    76       60    127    321\n",
       "14        49    72       58    126    305\n",
       "15        68    77       62    132    339\n",
       "16        58    52       58    109    277\n",
       "17        60    81       48     65    254\n",
       "18        49    50       50     63    212\n",
       "19        61    59       50    118    288\n",
       "20        63   108       65     97    333\n",
       "21        53   114       48     60    275\n",
       "22        46    64       46    119    275\n",
       "23        66    89       48    129    332\n",
       "24        48    79       50    126    303\n",
       "25        60   115       45    101    321\n",
       "26        52    89       49    115    305\n",
       "27        61    52       42     66    221\n",
       "28        49    92       45    102    288\n",
       "29        45   101       43    136    325\n",
       "30        67    88       57    116    328\n",
       "31        54    56       58    104    272\n",
       "32        57    97       63    103    320\n",
       "33        50    50       50    129    279\n",
       "34        61    71       58     88    278\n",
       "35        69   113       60    119    361\n",
       "36        67    79       45    108    299\n",
       "37        66   117       61     84    328\n",
       "38        46   101       64    135    346\n",
       "39        48    63       62    125    298\n",
       "40        49    91       40    101    281"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "politics=45+np.random.randint(25,size=40)\n",
    "math=50+np.random.randint(70,size=40)\n",
    "english=40+np.random.randint(26,size=40)\n",
    "major=60+np.random.randint(80,size=40)\n",
    "data={'politics':politics,'math':math,'english':english,'major':major}\n",
    "df=pd.DataFrame(data,index=range(1,41))\n",
    "df['total']=df.sum(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0586a11c-6cbd-418d-8792-b9e8b42b44aa",
   "metadata": {},
   "source": [
    "# pandas文件读写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "65316bfe-204d-42d2-be6e-39d36462745f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('data.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d25a76e1-acdb-4259-8827-2678721c0d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cc207879-d452-46b7-803d-8235f89049e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "144           6.7          3.3           5.7          2.5  virginica\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "16038da8-711c-42e0-8704-64f868aa63c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "546e7d74-7da7-45c9-8f3a-840946f51ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "df=pd.read_csv('data.csv')\n",
    "\n",
    "species=['setosa','versicolor','virginica']\n",
    "colors=['red','yellow','green']\n",
    "\n",
    "fig, axes = plt.subplots(2,2,figsize=(16, 10)) \n",
    "\n",
    "for i in range(3):\n",
    "    s=df[df['species']==species[i]]\n",
    "    cls=colors[i]\n",
    "    axes[0,0].scatter(s.iloc[:,0],s.iloc[:,1],c=cls,label=species[i],alpha=0.6)\n",
    "axes[0,0].set_xlabel('sepal_length')\n",
    "axes[0,0].set_ylabel('sepal_width')\n",
    "axes[0,0].legend()\n",
    "axes[0,0].grid(alpha=0.6)\n",
    "\n",
    "for i in range(3):\n",
    "    s=df[df['species']==species[i]]\n",
    "    cls=colors[i]\n",
    "    axes[0,1].scatter(s.iloc[:,0],s.iloc[:,2],c=cls,label=species[i],alpha=0.6)\n",
    "axes[0,1].set_xlabel('sepal_length')\n",
    "axes[0,1].set_ylabel('petal_length')\n",
    "axes[0,1].legend()\n",
    "axes[0,1].grid(alpha=0.6)\n",
    "\n",
    "for i in range(3):\n",
    "    s=df[df['species']==species[i]]\n",
    "    cls=colors[i]\n",
    "    axes[1,0].scatter(s.iloc[:,0],s.iloc[:,3],c=cls,label=species[i],alpha=0.6)\n",
    "axes[1,0].set_xlabel('sepal_length')\n",
    "axes[1,0].set_ylabel('petal_width')\n",
    "axes[1,0].legend()\n",
    "axes[1,0].grid(alpha=0.6)\n",
    "\n",
    "for i in range(3):\n",
    "    s=df[df['species']==species[i]]\n",
    "    cls=colors[i]\n",
    "    axes[1,1].scatter(s.iloc[:,1],s.iloc[:,3],c=cls,label=species[i],alpha=0.6)\n",
    "axes[1,1].set_xlabel('sepal_width')\n",
    "axes[1,1].set_ylabel('petal_width')\n",
    "axes[1,1].legend()\n",
    "axes[1,1].grid(alpha=0.6)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48b3b2aa-4202-4822-acaf-34aaef5dfbbe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
